Abstract

Automatic assessment of neurosensory retinal detachment (NRD) plays an important role in the diagnosis and treatment for central serous chorioretinopathy (CSC). In this paper, we propose a novel residual multiple pyramid pooling network (RMPPNet) to segment NRD in the spectral-domain optical coherence tomography (SD-OCT) images. Based on the encoder-decoder architecture, RMPPNet can better deal with receptive field and multi-scale features. In the encoder stage, based on the residual architectures, six striding convolutions are utilized to replace the conventional pooling layers to obtain wider receptive fields. To further explore the multi-scale features, three pyramid pooling modules (PPM) are supplemented in the encoder stage. In the decoder stage, we use multiple transpose convolutions to recover the resolution of feature maps and concatenate the feature maps from the encoder for each transpose convolution layer. Finally, for better and faster training, we propose a novel loss function to constrain the different sets between the true label and the prediction label. Three different datasets are utilized to evaluate the proposed model. The first dataset contains 35 cubes from 23 patients, and all the cubes are diagnosed as CSC with only NRD lesions. Based on the first dataset, the second dataset supplements ten normal cubes without NRD lesions. The proposed model obtains a mean dice similarity coefficient 92.6 ± 5.6 and 90.2 ± 20.5, respectively. The last dataset includes 23 cubes from 12 eyes of 12 patients with NRD lesions. The average quantitative results, i.e., mean true positive volume fraction, positive predictive value and dice similarity coefficient, obtained by the proposed model are 96%, 96.45% and 96.2%, respectively. The proposed model can provide a wider receptive field and more abundant multi-scale features to overcome the defects involved in NRD segmentations, such as various size, low contrast, and weak boundaries. Comparing with state-of-the-art methods, the proposed RMPPNet can produce more reliable results for NRD segmentation with higher mean values and lower standard deviations of quantitative criterion, which indicates the practical application for the clinical diagnosis of CSC.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (2)

K. Gao, S. Niu, Z. Ji, M. Wu, Q. Chen, R. Xu, S. Yuan, W. Fan, Y. Chen, and J. Dong, “Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images,” Comput. Meth. Prog. Bio. 176, 69–80 (2019).
[Crossref]

J. Hu, Y. Chen, and Z. Yi, “Automated segmentation of macular edema in OCT using deep neural networks,” Med. Image Anal. 55, 216–227 (2019).
[Crossref]

2018 (3)

L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018).
[Crossref]

M. Wu, Q. Chen, X. J. He, P. Li, W. Fan, S. T. Yuan, and H. Park, “Automatic subretinal fluid segmentation of retinal SD-OCT images with neurosensory retinal detachment guided by Enface fundus imaging,” IEEE Trans. Biomed. Eng. 65(1), 87–95 (2018).
[Crossref]

V. Chaikitmongkol, P. Khunsongkiet, D. Patikulsila, M. Ratanasukon, N. Watanachai, C. Jumroendararasame, C. B. Mayerle, I. C. Han, C. J. Chen, P. Winaikosol, C. Dejkriengkraikul, J. Choovuthayakorn, P. Kunavisarut, and N. M. Bressler, “Color Fundus Photography, Optical Coherence Tomography, and Fluorescein Angiography in Diagnosing Polypoidal Choroidal Vasculopathy,” Am. J. Ophthalmol. 192, 77–83 (2018).
[Crossref]

2017 (8)

C. R. G. Dreher, N. Kulp, C. Mandery, M. Wachter, and T. Asfour, “A framework for evaluating motion segmentation algorithms,” IEEE-RAS Int Conf Humanoid Robot 30(2), 83–90 (2017).
[Crossref]

P. Agrawal, “Increased Choroidal Vascularity in Central Serous Chorioretinopathy Quantified Using Swept-Source Optical Coherence Tomography,” Am. J. Ophthalmol. 174, 176–177 (2017).
[Crossref]

M. Wu, W. Fan, Q. Chen, Z. Du, X. Li, S. Yuan, and H. Park, “Three-dimensional continuous max flow optimization-based serous retinal detachment segmentation in SD-OCT for central serous chorioretinopathy,” Biomed. Opt. Express 8(9), 4257 (2017).
[Crossref]

Y. Xu, K. Yan, J. Kim, X. Wang, C. Li, L. Su, S. Yu, X. Xu, and D. D. Feng, “Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy,” Biomed. Opt. Express 8(9), 4061 (2017).
[Crossref]

V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732 (2017).
[Crossref]

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627 (2017).
[Crossref]

A. Montuoro, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and H. Bogunović, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8(3), 1874 (2017).
[Crossref]

2016 (5)

T. Hassan, M. Usman Akram, B. Hassan, A. M. Syed, and S. A. Bazaz, “Automated segmentation of subretinal layers for the detection of macular edema,” Appl. Opt. 55(3), 454 (2016).
[Crossref]

T. Wang, Z. Ji, Q. Sun, Q. Chen, S. Yu, W. Fan, S. Yuan, and Q. Liu, “Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images,” Inf. Sci. 358-359, 92–111 (2016).
[Crossref]

K. K. Dansingani, C. Balaratnasingam, S. Mrejen, M. Inoue, K. B. Freund, J. M. Klancnik, and L. A. Yannuzzi, “Annular Lesions and Catenary Forms in Chronic Central Serous Chorioretinopathy,” Am. J. Ophthalmol. 166, 60–67 (2016).
[Crossref]

J. Wang, M. Zhang, A. D. Pechauer, L. Liu, T. S. Hwang, D. J. Wilson, D. Li, and Y. Jia, “Automated volumetric segmentation of retinal fluid on optical coherence tomography,” Biomed. Opt. Express 7(4), 1577 (2016).
[Crossref]

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” SPIE Med. Imaging 9784, 97841C (2016).
[Crossref]

2015 (4)

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3-D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imaging 34(2), 441–452 (2015).
[Crossref]

A. Daruich, A. Matet, A. Dirani, E. Bousquet, M. Zhao, N. Farman, F. Jaisser, and F. Behar-Cohen, “Central serous chorioretinopathy: Recent findings and new physiopathology hypothesis,” Prog. Retinal Eye Res. 48, 82–118 (2015).
[Crossref]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172 (2015).
[Crossref]

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in OCT,” Biomed. Opt. Express 6(1), 155 (2015).
[Crossref]

2014 (3)

J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans Pattern Anal Mach Intell 39(4), 640–651 (2014).
[Crossref]

R. Hua, L. Liu, C. Li, and L. Chen, “Evaluation of the effects of photodynamic therapy on chronic central serous chorioretinopathy based on the mean choroidal thickness and the lumen area of abnormal choroidal vessels,” Photodiagn. Photodyn. Ther. 11(4), 519–525 (2014).
[Crossref]

M. Y. Teke, U. Elgin, P. Nalcacioglu-Yuksekkaya, E. Sen, P. Ozdal, and F. Ozturk, “Comparison of autofluorescence and optical coherence tomography findings in acute and chronic central serous chorioretinopathy,” Int. J. Ophthalmol. 7(2), 350–354 (2014).
[Crossref]

2013 (3)

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

T. Sekiryu, “Fundus autofluorescence in central serous chorioretinopathy,” Japanese J Clin Ophthalmol 67(2), 150–155 (2013).
[Crossref]

Y. Zheng, J. Sahni, C. Campa, A. N. Stangos, A. Raj, and S. P. Harding, “Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain optical coherence tomography images of the retina,” Am. J. Ophthalmol. 155(2), 277–286.e1 (2013).
[Crossref]

2012 (2)

S. J. Ahn, T. W. Kim, J. W. Huh, H. G. Yu, and H. Chung, “Comparison of features on SD-OCT between acute central serous chorioretinopathy and exudative age-related macular degeneration,” Ophthalmic Surg. Lasers Imaging 43(5), 374–382 (2012).
[Crossref]

G. R. Wilkins, O. M. Houghton, and A. L. Oldenburg, “Automated segmentation of intraretinal cystoid fluid in optical coherence tomography,” IEEE Trans. Biomed. Eng. 59(4), 1109–1114 (2012).
[Crossref]

2011 (1)

S. Vujosevic, M. Casciano, E. Pilotto, B. Boccassini, M. Varano, and E. Midena, “Diabetic macular edema: Fundus autofluorescence and functional correlations,” Invest. Ophthalmol. Visual Sci. 52(1), 442–448 (2011).
[Crossref]

2010 (1)

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: Identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref]

2006 (1)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - A graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref]

2005 (1)

D. C. Fernández, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans. Med. Imaging 24(8), 929–945 (2005).
[Crossref]

1985 (1)

A. R. Irvine, “The pathogenesis of aphakic retinal detachment,” Ophthalmic Surg 16(2), 101–107 (1985).

Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Abràmoff, M. D.

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: Identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref]

Adam, H.

L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking Atrous Convolution for Semantic Image Segmentation,” (2017).

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) (2018), pp. 801–818.

Agrawal, P.

P. Agrawal, “Increased Choroidal Vascularity in Central Serous Chorioretinopathy Quantified Using Swept-Source Optical Coherence Tomography,” Am. J. Ophthalmol. 174, 176–177 (2017).
[Crossref]

Ahn, S. J.

S. J. Ahn, T. W. Kim, J. W. Huh, H. G. Yu, and H. Chung, “Comparison of features on SD-OCT between acute central serous chorioretinopathy and exudative age-related macular degeneration,” Ophthalmic Surg. Lasers Imaging 43(5), 374–382 (2012).
[Crossref]

Allingham, M. J.

Al-Louzi, O.

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Figures (11)

Fig. 1.
Fig. 1. SD-OCT retinal image with NRD.
Fig. 2.
Fig. 2. Structure of the Residual Multiple Pyramid Pooling Net (RMPPNet).
Fig. 3.
Fig. 3. Visual comparison of the detection results on four example cubes in the first dataset. Each bar indicates the detection results on the 3D cubes with 128 B-scans, in which the green and blue colors respectively indicate the correct detections for the B-scans with/without NRD, and the red color shows the false detections. For each subfigure, the bars from top to bottom shows the detection results obtained by FCN, SegNet, UNet, FastFCN, PSPNet, DeepLabV3+ and our model, respectively.
Fig. 4.
Fig. 4. The segmentation results on the selected B-scans from the cubes in Fig. 3. The green line is the boundary of the label ground truth, the red line is the boundary of the predictions for each method, and the yellow line is the overlapping part of the red line and the green line. In each row, the images show the segmentation results obtained by FCN, SegNet, UNet, FastFCN, PSPNet, DeepLabV3+ and our model, respectively.
Fig. 5.
Fig. 5. Visual comparison of the detection results on four example cubes in the second dataset. Each bar indicates the detection results on the 3D cubes with 128 B-scans, green color and blue color respectively indicate the correct detections for the B-scans with/without NRD, and the red color shows the false detections. For each subfigure, the bars from top to bottom show the detection results obtained by FCN, SegNet, UNet, FastFCN, PSPNet, DeepLabV3+ and our model, respectively.
Fig. 6.
Fig. 6. The segmentation results on the selected B-scans from the cubes in Fig. 5. The green line is the boundary of the label ground truth, the red line is the boundary of the predictions for each method, and the yellow line is the overlapping part of the red line and the green line. In each row, the images show the segmentation results obtained by FCN, SegNet, UNet, FastFCN, PSPNet, DeepLabV3+ and our model, respectively.
Fig. 7.
Fig. 7. The segmentation results obtained by all the comparison methods on four example B-scans selected from four different patients. The red line is the boundary segmentation results, the green and blue lines are the boundaries of ground truths drawn by the first and second expert, and the yellow line is the overlapping part between the predictions and two ground truths. The images beside each B-scans show the enlarged view of the targets.
Fig. 8.
Fig. 8. Statistical correlation analysis. Figures (a) and (c) show the linear regression analysis between the proposed method and Expert 1/2, respectively. Figures (b) and (d) show the Bland-Altman plot for the proposed method and Expert 1/2, respectively.
Fig. 9.
Fig. 9. 3D surfaces of the segmentations obtained by all the comparison methods on eight example cases selected from eight patients. The images from the first row to the ninth row are the segmentation surfaces obtained by LPHC, SS-KNN, RF, FLSCV, CMF, EFD, Blob, DBFCN and the proposed model, respectively. The images in the last two rows show the manual segmentation surfaces of Expert 1 and Expert 2, respectively.
Fig. 10.
Fig. 10. The comparison results between the ground truths and the segmentation results obtained by the proposed method on six example B-scans. In each subfigure, the image beside the B-scan shows the enlarged view of the rectangle region marked by a green box, in which the yellow area is the true-positive segmentations, the green and red areas are the under and over segmentations obtained by the proposed method, respectively.
Fig. 11.
Fig. 11. The segmentation results on two example B-scans with four different training-testing strategies. In each row, the images from left to right shows the original B-scans, the enlarged segmentation results obtained by Tr.E1&Te.E1 strategy, Tr.E1&Te.E2 strategy, Tr.E2&Te.E1 strategy and Tr.E2&Te.E2 strategy, respectively. All the segmentation maps are the enlarged views of the rectangle region marked by a green box in the B-scan image, in which the yellow area is the true-positive segmentations, the green and red areas are the under and over segmentations obtained by the proposed method, respectively.

Tables (6)

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Table 1. The segmentation accuracies with different setting of PPMs.

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Table 2. The segmentation accuracies with different combinations of loss functions.

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Table 3. Statistical results (mean ± standard deviation) of DSC index with five-fold cross-validation on the first dataset.

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Table 4. Statistical results (mean ± standard deviation) of DSC index with five-fold cross-validation on the second dataset.

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Table 5. The quantitative results (mean ± standard deviation) between the segmentations and two independent ground truths.

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Table 6. The quantitative results (mean ± standard deviation) of the proposed method by using different training and testing ground truths.

Equations (7)

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L B C E = 1 m 1 m ( 1 n 1 n ( y t r u t h l o g y p r e d ( 1 y t r u t h ) log ( 1 y p r e d ) ) )
L D I C E = 1 m 1 m ( 1 2 G t r u t h G p r e d + θ G t r u t h + G p r e d + θ )
L D I F = 1 m 1 m ( G t r u t h + G p r e d 2 G t r u t h G p r e d )
L = L B C E + λ 1 L D I C E + λ 2 L D I F
T P V E = V t r u t h V p r e d V t r u t h
P P V = V t r u t h V p r e d V p r e d
D S C = 2 V t r u t h V p r e d V t r u t h + V p r e d